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35th IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Avila, Andres I. Manuel Aedo, Juan Gonzalez-Flores, Mariela Univ La Frontera Dept Ingn Matemat Temuco Chile Univ Aysen Dept Cs Nat & Tecnol Coyhaique Chile

ISBN: (纸本)9798350381603

The most used problem to compare these metaheuristics is the classical Travelling Salesman Problem (TSP) on a network with sizes from tens to millions of nodes. Nature-based metaheuristics are the main source of optimization techniques to solve this problem. Although there is a zoo of possibilities, ant colony optimization (ACO) is still one of the most efficient, parallel, and simple techniques to implement. The pheromone evaporation rate, alpha,beta, and ant number M are the four parameters fitted for finding the best solutions. Candidate list and the use of a source solution are efficient strategies to optimize large problems, but there are other strategies to improve quality solutions such as local search strategies. Among the local search techniques, k-opt search has proved to be very efficient to deal with path-crossing. The initial route is split into k sub-routes connected in (k- 1)!2(k-1) ways. Thus, 3-opt is an efficient strategy balancing complexity and precision. Moreover, the best alpha and beta are the result of the used strategy. Finally, the solution is improved by accelerating through parallel versions with multiGPUs. In this article, four ACO algorithms were developed using two variations (Rank Based 3-opt and Strong Elitist 3-opt), each one with two strategies (candidate list and restricted). To test the algorithms, TSPLIB95 was used with test problems between N = 51 and 4, 461 nodes in a server with 4 RTX A4000 GPUs. After improving the algorithms and parallelization, the parameters are tuned to get the highest performance improving the local minimum search. Statistical analysis of repetitions shows good stability of the chosen metaheuristics.

关键词： Multi-GPU ant colony optimization algorithms metaheuristics local search high performance

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A Clustering Routing Algorithm Based on Improved ant colony optimization algorithms for Underwater Wireless Sensor Networks

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algorithms 2020年第10期13卷 250-250页

作者： Xiao, Xingxing Huang, Haining Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Sci & Technol Adv Underwater Acoust Signa Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in underwater wireless sensor networks (UWSNs), this paper proposes an energy-efficient clustering routing algorithm based on an improved ant colony optimization (ACO) algorithm. In clustering routing algorithms, the network is divided into many clusters, and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs). This paper optimizes the CHN selection based on the residual energy of nodes and the distance factor. The selected CHN gathers data sent by the CMNs and transmits them to the sink node by multiple hops. Optimal multi-hop paths from the CHNs to the SN are found by an improved ACO algorithm. This paper presents the ACO algorithm through the improvement of the heuristic information, the evaporation parameter for the pheromone update mechanism, and the ant searching scope. Simulation results indicate the high effectiveness and efficiency of the proposed algorithm in reducing the energy consumption, prolonging the network lifetime, and decreasing the packet loss ratio.

关键词： underwater wireless sensor networks ant colony optimization algorithms clustering routing algorithms energy efficiency network lifetime

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ant colony optimization algorithms for Digital Device Diagnostics

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2015年第2期49卷 82-87页

作者： Speranskii, D. V. Moscow State Univ Railway Engn Ul Chasovaya 22-2 Moscow 125993 Russia

This paper investigates the possibility of solving two problems of digital device diagnostics by means of ant colony optimization. It is shown how these problems can be reduced to the classical traveling salesman and ... 详细信息

关键词： ant colony optimization algorithms digital devices diagnostics

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A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

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BIOINSPIRATION & BIOMIMETICS 2014年第3期9卷 036006-36005页

作者： Zhang, Zili Gao, Chao Liu, Yuxin Qian, Tao Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3217 Australia

ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.

关键词： Physarum ploycephalum Physarum-inspired model ant colony optimization algorithms travelling salesman problem

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A Resampling ant colony optimization with Elite Exploration and Convergence Mechanism for Multithreshold Segmentation of Breast Cancer Images

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2024年第9期6卷

作者： Wang, Zhen Zhao, Dong Heidari, Ali Asghar Chen, Yi Chen, Huiling Liang, Guoxi Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran 999067 Iran Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. Addressing this, the ant colony optimization algorithm for continuous optimization (ACOR) shows promise. Yet, existing ACOR variants still grapple with poor initial population quality, affecting convergence speed and avoiding local optimization. These issues impact segmentation efficiency and accuracy. To tackle them, this study introduces RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local optima entrapment and enhancing model stability. SCM expediting convergence, segmentation efficiency, and precision. RESACO's performance is assessed through extensive experiments using IEEE CEC 2014 and IEEE CEC 2022 benchmark functions, including ablation experiments and comparisons with basic and improved algorithms and ACOR variants. Subsequently, the threshold image segmentation model based on RESACO is compared with other models using metaheuristic algorithms for segmenting realistic breast cancer medical images. Results demonstrate the proposed model's faster convergence and higher segmentation accuracy, preserving more lesion tissue details. RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local

关键词： ant colony optimization algorithms breast cancers metaheuristic algorithms threshold image segmentations

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Intelligence-Based Multistage optimization Framework for Multiple Debris Removal Mission

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第7期47卷 1447-1456页

作者： Zhu, Yue-He Zhang, Yu Luo, Ya-Zhong Zhang, Jia-Cheng Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

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Two-Stage Dynamic-Assignment optimization Method for Multispacecraft Debris Removal

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第9期45卷 1750-1759页

作者： Zhang, Nan Chen, Shiyu Zhang, Zhong Baoyin, Hexi Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China Beijing Inst Spacecraft Syst Engn Beijing 100094 Peoples R China

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Global optimization of Multiple-Spacecraft Rendezvous Mission via Decomposition and Dynamics-Guide Evolution Approach

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第1期45卷 171-178页

作者： Huang, An-yi Luo, Ya-zhong Li, Heng-nian Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Xian Satellite Control Ctr Xian 710043 Peoples R China Hunan Key Lab Intelligent Planning & Simulat Aero Changsha 410073 Peoples R China

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optimization for Multitarget, Multispacecraft Impulsive Rendezvous Considering J2 Perturbation

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第10期44卷 1811-1822页

作者： Chen, Shiyu Jiang, Fanghua Li, Haiyang Baoyin, Hexi Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

optimization for active debris removal using multiple spacecraft is investigated. The main challenge is to determine the rendezvous sequences of the targets considering the J(2) perturbation, which is a large-scale dynamic combinatorial optimization problem. A framework to solve this problem is presented. First, a semi-analytical method of fast estimation for characteristic velocity is proposed to help construct the sequences. The K-medoid method is applied to distribute all the targets to multiple spacecraft so that each spacecraft can remove as many targets as possible with limited propellant. Given a distribution of targets, the sequences for each spacecraft are searched separately;thus the original combinatorial optimization problem is split into multiple small-scale ones that can be efficiently solved by tree search algorithms. An evolutionary algorithm nested with a tree search algorithm is proposed to improve the distribution of the targets, so that the spacecraft can remove all the targets with a low cost. Then optimal sequences are further searched using an ant colony optimization algorithm, and the rendezvous epochs are refined by a nonlinear programming algorithm. The efficiency of these methods is demonstrated by two scenarios of active debris removal.

关键词： Propellant Characteristic Velocity Nonlinear Programming Particle Swarm optimization Search Algorithm ant colony optimization algorithms Trajectory optimization Earth Orbital Elements Orbital Period

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Hybrid Approaches based on Simulated Annealing, Tabu Search and ant colony optimization for Solving the k-Minimum Spanning Tree Problem

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第2期12卷 708-712页

作者： Addou, El Houcine Serghini, Abelhafid Mermri, El Bekkaye Mohammed I Univ LANO Lab FSO ESTO BV Mohamed VI BP 717 Oujda 60000 Morocco Mohammed I Univ FSO BV Mohamed VI BP 717 Oujda 60000 Morocco

In graph theory, the k-minimum spanning tree problem is considered to be one of the well-known NP hard problems to solve. This paper address this problem by proposing several hybrid approximate approaches based on the combination of simulated annealing, tabu search and ant colony optimization algorithms. The performances of the proposed methods are compared to other approaches from the literature using the same well-known library of benchmark instances.

关键词： k-Minimum spanning tree metaheuristics simulated annealing ant colony optimization algorithms tabu search approximation algorithms

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